What the data for this category looks like
Chemical raw material data for intelligent due diligence reports comes primarily from industry association public annual reports, customs import and export statistics, public disclosure documents from production enterprises, MSDS (Material Safety Data Sheets) issued by third-party testing institutions, and product testing reports. Data update cycles vary by source: monthly (customs data), quarterly (industry capacity data), and product change-triggered updates (MSDS). Document structure includes fields such as product name, CAS registry number, molecular formula, physical and chemical parameters, production capacity and output, import and export volume, and price trends. Units include professional measurement standards such as g/cm³, ℃, tons/year, and yuan/kg.
What constraints these characteristics impose on vector models and indexing
Chemical raw material data contains dense technical terms, including exclusive identifiers such as CAS numbers and toxicity grades. This requires vector models to have professional domain semantic adaptation capabilities. Differences in update cycles across multiple sources mean indexing must support incremental updates to avoid the high time cost of full reconstruction. Documents include long text descriptions and structured fields, requiring separate handling of semantic blocks and metadata to avoid breaking the integrity of technical terms through single-segment splitting. Additionally, the category has wide coverage, with large data volumes for single knowledge bases. Indexing must support sharded deployment to maintain retrieval concurrency.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Chemical raw material documents have long professional sentences. Segments that are too long lose semantic connections, while segments that are too short damage the integrity of technical terms |
embedding_model | Vector model adapted to the chemical engineering professional domain | General vector models have insufficient semantic matching accuracy for professional fields such as CAS numbers and toxicity terms |
incremental_index_enabled | Enabled | Chemical raw material data updates monthly or quarterly. Incremental indexing reduces reconstruction time |
recall_top_k | Top 10–15 results | Due diligence reports require coverage of multi-dimensional raw material parameters. Too many recalled results increase context length, while too few miss key information |
similarity_threshold | Calibrated via testing for professional scenarios | Semantic similarity thresholds for technical terms are higher than for general scenarios. Adjust based on actual retrieval needs |
index_shard_num | Set to 2–3 times the number of cluster nodes | Chemical raw material knowledge bases have large data volumes. Sharding improves retrieval concurrency |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- Symptom: The knowledge base remains in the "Indexing" status for an extended period after importing a dataset, with no progress. Cause: The incremental index switch is not enabled. Full indexing takes too long when processing large volumes of long-text chemical raw material documents.
- Symptom: Retrieval returns a similarity value over 10000. Cause: The similarity threshold numerical range is not configured correctly. Unnormalized raw model output is used by mistake.
- Symptom: Retrieval speed does not improve, or even decreases, in multi-replica deployments. Cause: Load balancing for index shards is not configured. Multiple replicas accessing the same index shard simultaneously causes resource contention.
How to confirm the configuration is correct
- Execute a single indexing task, and check the task log for successful segmentation records to confirm the segmentation parameters match document lengths.
- Submit a retrieval request containing technical terms, and review the similarity value range of returned results to confirm the threshold configuration meets professional scenario requirements.
- Simulate an incremental update scenario, verify that only newly added documents are added to the index without a full reconstruction, to confirm the incremental index switch is active.
- After deploying multiple replicas, check cluster monitoring resource usage to confirm shard load balancing configuration is active.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.